Sunday, May 28, 2017

And there's more

Google systems based on neural networks can adjust their behaviour to improve, learn and evolve. For example, a voice recognition system could give different results depending on the person using it, for example by using their search history to help understand what the user has said. Ultimately it all comes down to reducing conversion errors, and probability plays a big role in this.

WHEN GOOGLE BUILT the latest version of its Android mobile operating system, the web giant made some big changes to the way the OS interprets your voice commands. It installed a voice recognition system based on what’s called a neural network — a computerized learning system that behaves much like the human brain.

For many users, says Vincent Vanhoucke, a Google research scientist who helped steer the effort, the results were dramatic. “It kind of came as a surprise that we could do so much better by just changing the model,” he says.

Vincent Vanhoucke is a Principal Scientist at Google. He is a technical lead in the Google Brain Team and manages Google's Robotics Research effort. Prior to that, he lead Brain's vision and perception research, and the speech recognition quality team for Google Search by Voice. He holds a Ph.D. in Electrical Engineering from Stanford University and a Diplôme d'Ingénieur from the Ecole Centrale Paris.

Google owns Blogger, so I guess the Google Search by Voice network will now be aware that I am researching it. How's that for recursion.